Placement　has　a　tremendous　impact　on　the　final　performance　of　a　VLSI　chip,　and　the　problem　itself　is　a　combinatorial　optimization　problem.　Memetic　algorithms　with　problem-specific　designs　can　solve　the　problem　with　high　quality　solutions,　but　they　are　generally　time-consuming.　To　address　this　issue,　adopting　an　adaptive　strategy　has　become　a　key　way　to　reduce　the　runtime　of　a　memetic　algorithm.　In　this　paper,　we　present　an　adaptive　multimeme　memetic　algorithm　(AMMA)　for　the　VLSI　standard　cell　placement　problem.　In　accordance　with　the　distinctive　features　of　the　solution　space　of　the　problem,　we　propose　a　set　of　adaptive　strategies　to　greatly　improve　the　runtime　efficiency.　The　main　novelty　of　the　proposed　strategy　lies　in　its　constructing　of　crossover　multimeme　and　the　acceptance　criteria　of　placement　candidates.　The　experimental　results　and　comparisons　on　Peko　suite3　and　ISPD04　benchmark　circuits　verified　the　efficacy　and　efficiency　of　the　proposed　adaptive　strategies.　Compared　to　the　memetic　algorithm　without　these　main　adaptive　strategies,　the　AMMA　reduces　the　runtime　by　around　32.58%　on　average.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2021.